Introduction of Project in Neural Network

In this project, an investigation was carried out using neural networks and knowledge-based systems to recognize, validate and interpret handwritten characters.

Neural network is one of the techniques used in artificial intelligent. It consists of many nonlinear computational elements that form the network neurons, linked by weighted interconnections.

The neurons are designed similar to the neurological system of animals. The networks are most effective in performing tasks like classification and error correction.

There are a few kinds of neural networks and those that are used in this project are MLP, RBF and Hopfield networks. They are being compared to see which is the most suitable for character recognition. Data files with different resolutions and data sets are used to train and test out the network.

Knowledge-based systems are intelligent systems that are build with the flexibility of adding new knowledge to the program without affecting the whole system. A rule-based system is a knowledge-based system where the knowledge-base is represented in the form of a sets of rules where rules are an flexible means of expressing knowledge.

This project uses a rule-based system to first validate a vehicle registration mark entered by user and then interpret the marks to identify where and when the vehicle is registered. Rules are also written to suggest alternative for misidentify characters. For example, a character ‘0’ are incorrectly identify as ‘O’ and these rules will change the character ‘O’ back to ‘0’. An agent-based system was also worked in conjunction with the rule-based system to validate vehicle registration marks.